Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

The brain's electrical signals enable people without muscle control to physically interact with the world.

Brain-computer interfaces for communication and control

Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. John Donoghue and colleagues have previously demonstrated that people with tetraplegia can learn to use neural signals from the motor cortex to control a computer cursor. Work from another lab has also shown that monkeys can learn to use such signals to feed themselves with a robotic arm. Now, Donoghue and colleagues have advanced the technology to a level at which two people with long-standing paralysis — a 58-year-old woman and a 66-year-old man — are able to use a neural interface to direct a robotic arm to reach for and grasp objects. One subject was able to learn to pick up and drink from a bottle using a device implanted 5 years earlier, demonstrating not only that subjects can use the brain–machine interface, but also that it has potential longevity. Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system1,2,3,4,5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices6,7,8. Able-bodied monkeys have used a neural interface system to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

/pdf/reach-and-grasp-by-people-with-tetraplegia-using-a-neurally-u1xyn3on0d.pdf

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Brain-machine interfaces have mostly been used previously to move cursors on computer displays. Now experiments on macaque monkeys show that brain activity signals can control a multi-jointed prosthetic device in real-time. The monkeys used motor cortical activity to control a human-like prosthetic arm in a self-feeding task, with a greater sophistication of control than previously possible. This work could be important for the development of more practical neuro-prosthetic devices in the future. A system where monkeys use their motor cortical activity to control a robotic arm in a real-time self-feeding task, showing a significantly greater sophisitication of control than in previous studies, is demonstrated. This work could be important for the development of more practical neuro-prosthetic devices in the future. Arm movement is well represented in populations of neurons recorded from the motor cortex1,2,3,4,5,6,7. Cortical activity patterns have been used in the new field of brain–machine interfaces8,9,10,11 to show how cursors on computer displays can be moved in two- and three-dimensional space12,13,14,15,16,17,18,19,20,21,22. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment (‘embodiment’) has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects’ cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control23, previous experiments have lacked physical interaction even in cases where a robotic arm16,19,24 or hand20 was included in the control loop, because the subjects did not use it to interact with physical objects—an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.

/pdf/cortical-control-of-a-prosthetic-arm-for-self-feeding-1ethevoxtu.pdf

Cortical control of a prosthetic arm for self-feeding

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

/pdf/brain-computer-interfaces-a-review-2z42gnpdfy.pdf

Brain Computer Interfaces, a Review

Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a ‘neural cursor’ with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis. The cover shows Matt Nagle, first participant in the BrainGate pilot clinical trial. He is unable to move his arms or legs following cervical spinal cord injury. Researchers at the Department of Neuroscience at Brown University, working with biotech company Cyberkinetics and 3 other institutions, demonstrate that movement-related signals can be relayed from the brain via an implanted BrainGate chip, allowing the patient to drive a computer screen cursor and activate simple robotic devices. Such neuromotor prostheses could pave the way towards systems to replace or restore lost motor function in paralysed humans. Prior to this advance, this type of work has been performed chiefly in monkeys. The latest example of such research has achieved a large increase in speed over current devices, enhancing the prospects for clinically viable brain-machine interfaces.

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

Various embodiments of a biological interface system and related methods are disclosed. The system may comprise a sensor comprising a plurality of electrodes for detecting multicellular signals emanating from one or more living cells of a patient and a processing unit configured to receive the multicellular signals from the sensor and process the multicellular signals to produce a processed signal. The processing unit may be configured to transmit the processed signal to a controlled device that is configured to receive the processed signal. The system is configured to perform an integrated patient training routine to generate one or more system configuration parameters that are used by the processing unit to produce the processed signal.

Patient training routine for biological interface system

Primary motor cortex (MI), a key region for voluntary motor control, has been considered a first choice as the source of neural signals to control prosthetic devices for humans with paralysis. Less is known about the potential for other areas of frontal cortex as prosthesis signal sources. The frontal cortex is widely engaged in voluntary behavior. Single neuron recordings in monkey frontal cortex beyond MI have readily identified activity related to planning and initiating movement direction, remembering movement instructions over delays, or mixtures of these features (Kurata & Wise, 1988; Boussaoud & Wise, 1993; Crammond & Kalaska, 1994, 2000). Human functional imaging and lesion studies also support this role (Toni et al., 1999; Simon et al., 2002). Intraoperative mapping during deep brain stimulator placement in humans (Benabid et al., 1989) provides a unique opportunity to evaluate potential prosthesis control signals derived from non-primary areas and to expand our understanding of frontal lobe function and its role in movement disorders. Here we show that recordings from small groups of human prefrontal/premotor cortex neurons can provide information about movement planning, production and decision making sufficient to decode the planned direction of movement. Thus, additional frontal areas, beyond M1, may be valuable signal sources for human neuromotor prostheses.

/pdf/decoding-movement-intent-from-human-premotor-cortex-neurons-korifhbsd6.pdf

Decoding movement intent from human premotor cortex neurons for neural prosthetic applications.

A system and method for a neural interface system with a unique identification code includes a sensor including a plurality of electrodes to detect multicellular signals, an processing unit to process the signals from the sensor into a suitable control signal for a controllable device such as a computer or prosthetic limb. The unique identification code is embedded in one or more discrete components of the system. Internal and external system checks for compatibility and methods of ensuring safe and effective performance of a system with detachable components are also disclosed.

Neural interface system and method for neural control of multiple devices

Various embodiments of a biological interface system and related methods are disclosed. The system may include a sensor having a plurality of electrodes for detecting multicellular signals emanating from one or more living cells of a patient, and a processing unit configured to receive the multicellular signals from the sensor and process the multicellular signals to produce a processed signal. The processing unit may be configured to transmit the processed signal to a controlled device that is configured to receive the processed signal. The system may also include a patient training apparatus configured to receive a patient training signal that causes the patient training apparatus to controllably move one or more joints of the patient. The system may be configured to perform an integrated patient training routine to produce the patient training signal, to store a set of multicellular signal data detected during a movement of the one or more joints, and to correlate the set of multicellular signal data to a second set of data related to the movement of the one or more joints.

Abraham H. Caplan

Papers

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

Patient training routine for biological interface system

Decoding movement intent from human premotor cortex neurons for neural prosthetic applications.

Neural interface system and method for neural control of multiple devices

Biological interface system with patient training apparatus